Method of processing image signal

ABSTRACT

R, G, B signals outputted from a digital camera or a color scanner are converted into colorimetric signals by a colorimetric converter having a colorimetric conversion matrix and a colorimetric conversion table. Colorimetric signals are processed for setup by a colorimetric setup unit. Colorimetric signals processed for setup are converted by a cmyk converter into c, m, y, k signals that are half tone dot % signals for being supplied to an image output unit such as a film producing device, a color printer, or the like.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method of processing an image signal,and more particularly to a method of converting an image signal such asR (red), G (green), B (blue) signals generated by an imaging device suchas a color digital camera (also referred to as a digital camera) andrepresenting an original scene which is captured by the imaging device,or an imaging signal such as R, G, B signals generated by an imagereading device such as a color scanner (also referred to as a scanner),into a colorimetric signal, processing a colorimetric signal for setup,converting a colorimetric signal processed for setup into a dye densitysignal for use on a reversal medium, or converting a colorimetric signalprocessed for setup into c, m, y, k (cyan, magenta, yellow, and black)signals for being supplied to an image output device such as a filmproducing device, a plate producing device, a color printer, or thelike.

2. Description of the Related Art

Device-dependent image signals such as R, G, B signals generated bycolor digital cameras are converted into device-independent imagesignals such as tristimulus signals X, Y, Z according to colorconversion processes disclosed in Japanese laid-open patent publicationsNos. 2-291777 and 2-291778, for example.

Techniques for predicting reproducible colors on prints, for example,device-independent tristimulus signals X, Y, Z from device-dependent c,m, y halftone dot % signals or reproducible colors based on colorsignals c, m, y, k are disclosed in Japanese laid-open patentpublications Nos. 4-337965 and 4-337966, for example.

According to a process of accurately converting colors as disclosed inthe Journal of the Institute of Image Electron, Vol. 18, No. 5 (1989), athree-dimensional (XYZ) color space is divided into 512 cubic regions,and color correcting values at a total of 729 grid points are calculatedin a process optimized for the characteristics of an output device andstored as the data of a lookup table (LUT). Input values between thegrid points are determined by an interpolating process whichthree-dimensionally interpolates the LUT.

According to another known process, R, G, B signals produced from acolor reversal subject (prepared by exposing a color reversal film tolight from an image, developing the image on the color reversal film)which carries a positive image are converted into equivalent neutraldensity (END) signals by a color scanner which is a linear scanningreader, and the END signals are converted into halftone dot % signalswith reference to setup points (highlight and shadow points) determinedby a setup process. The halftone dot % signals are then converted intoc, m, y, k signals, which are then binarized (i.e., converted intohalftone dots), finally producing a printing plate or a printedmaterial.

With conventional color scanners, R, G, B signals are processed forsetup based on color-separating default conditions (image processingconditions including at least a gradation conversion process, a colorcorrection process, an under color removal process, and a K-plategenerating process) which are carried on the respective color scannersand empirically optimized, and R, G, B signals produced from a colorreversal subject are converted into c, m, y, k signals.

With respect to R, G, B signals produced by a digital camera,color-separating default conditions may be established using an imageprocessing tool (software) such as Adobe Photoshop (registeredtrademark).

SUMMARY OF THE INVENTION

It is a major object of the present invention to provide a method ofprocessing an image signal, which can easily convert device-dependentimage signals into device-independent image signals.

Another object of the present invention is to provide a method ofprocessing an image signal, which allows a setup process based on ENDsignals, for which gray conditions are prescribed in a device-dependentsignal system, to be applied to a device-independent signal system.

Still another object of the present invention is to provide a method ofprocessing an image signal for producing c, m, y, k signals capable ofreproducing colors of an original scene with ease and accuracy.

Yet still another object of the present invention is to provide a methodof processing an image signal, which is capable of converting imagesignals of an original scene captured by a digital camera into colorsignals that can utilize empirically optimized standard defaultseparating conditions of an existing scanner.

A further object of the present invention is to provide a method ofprocessing an image signal, which will convert R, G, B image signalsgenerated by a digital camera or R, G, B image signals read by a scannerinto c, m, y, k signals using empirically optimized default separatingconditions of a particular existing scanner.

The above and other objects, features, and advantages of the presentinvention will become more apparent from the following description whentaken in conjunction with the accompanying drawings in which preferredembodiments of the present invention are shown by way of illustrativeexample.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an image signal processing apparatus whichcan carry out a method of processing an image signal according to thepresent invention;

FIG. 2 is a flowchart of a sequence for generating a colorimetricconversion matrix;

FIG. 3 is a flowchart of an operation sequence of a colorimetric setupunit;

FIG. 4 is a CIE chromaticity diagram illustrative of a colorreproduction range for reversal films;

FIG. 5 is a flowchart of a conversion process for converting acolorimetric signal of a digital camera into a dye density signal on areversal subject;

FIG. 6 is a flowchart of another conversion process for converting acolorimetric signal of a digital camera into a dye density signal on areversal subject;

FIG. 7 is a flowchart of a sequence for generating a dye densityconversion matrix;

FIG. 8 is a diagram illustrative of the Newton-Raphson method;

FIG. 9 is a flowchart of a portion of the sequence for generating a dyedensity conversion matrix;

FIG. 10 is a flowchart of a sequence for generating an accurate originalscene reproduction table; and

FIG. 11 is a flowchart of a sequence for generating a standard conditionreproduction table.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows an overall arrangement of an image signal processingapparatus 10 which can carry out a method of processing an image signalaccording to the present invention.

As shown in FIG. 1, the image signal processing apparatus 10 has acolorimetric converter 16 which will be supplied with R, G, B signals(digital image signals) 12 representing image information of a scenethat has been captured by a digital camera (not shown) as an imagingdevice, and also with R, G, B signals (digital image signals) 14representing image information of a reversal subject that has been readby an image input unit (which may be referred to as a linear scanningreader or a scanner input unit) of a scanner (not shown) as an imagereading device. The digital camera may be a digital video camera or anydevice capable of capturing image signals and outputting digital imagesignals (digital image data).

The scanner may be a linear scanning reader which is a color scannerincorporating a linear image sensor or an area scanning reader which isa color scanner incorporating an area image sensor.

The colorimetric converter 16 has a colorimetric conversion matrix(hereinafter simply referred to as a matrix) 20 for converting R, G, Bsignals 12 from a digital camera into X, Y, Z or L*, a*, b* colorimetricsignals (hereinafter referred to as X, Y, Z signals) 18, and acolorimetric conversion table (hereinafter referred to as a colorimetricconversion lookup table, a lookup table, or a table) 24 for convertingR, G, B signals from the scanner input unit into X, Y, Z signals or L*,a*, b* colorimetric signals 22.

Usually, the R, G, B, signals 12, 14 are called device-dependent signals(data), and the colorimetric signals 18, 22 are calleddevice-independent signals (data).

The colorimetric signals 18 originating from the digital camera issupplied, if necessary, to a light source changer 25 which absorbs thedifference between an imaging light source and an observing lightsource, a colorimetric setup unit (hereinafter also referred to as asetup unit) 26 for setting up a highlight area density and a shadow areadensity, a dye density converter 30 for converting the colorimetricsignals 18 of the original scene (captured scene) into dye densitysignals 28 on the reversal subject, and an accurate original scenereproduction table 42 of a cmyk converter 32.

The colorimetric signals 22 originating from the scanner input unit aresupplied to the colorimetric setup unit 26 and a dye density converter36 of a standard condition reproduction table unit 43, for convertingthe colorimetric signals 22 into dye density signals 34.

The dye density signals (c, m, y signals) 28, 34 outputted from the dyedensity converters 30, 36 are selected by a switch (multiplexer orselector) 45 and supplied to a standard condition reproduction table 44.

The cmyk converter 32 basically has the accurate original scenereproduction table 42 and the standard condition reproduction table unit43.

The accurate original scene reproduction table 42 comprises a lookuptable for converting the colorimetric signals 18 supplied from thedigital camera into c, m, y, k signals 46 which are halftone dot %signals that are colorimetrically saved.

The standard condition reproduction table unit 43 has the dye densityconverter 36 for converting the colorimetric signals 22 into dye densitysignals 34, the switch 45 for selecting either the dye density signals28 from the dye density converter 30 or the dye density signals 34signals from the dye density converter 36, and the standard conditionreproduction table 44. The standard condition reproduction table 44serves to convert either the dye density signals 28 or the dye densitysignals 34 into c, m, y, k signals 48 which are halftone dot % signals.The cmyk converter 32 has a three-color/four-color conversion functionfor converting signals in a trichromatic system into signals in atetrachromatic system.

The c, m, y, k signals 46, 48 which are halftone dot % signals outputtedfrom the cmyk converter 32 are supplied to an image output unit 35 whichoutputs an image based on the c, m, y, k signals 46, 48.

The image output unit 35 may be of a known nature which comprises abinary converter (not shown), a laser beam exposure scanner (imagesetter or the like), an image developing unit, a printing plategenerator, and a printing unit. The binary converter compares the c, m,y, k signals 46, 48 with C, M, Y, K threshold matrixes selecteddepending on output conditions representative of a screen ruling, ascreen angle, and so on for thereby converting the c, m, y, k signals46, 48 into binary signals. The laser beam exposure scanner applies alaser beam that is selectively turned on and off by the binary signals(also called binary image signals) to films to form latent images on thefilms. The image developing unit develops the latent images on thefilms, producing platemaking films. The printing plate generator thengenerates printing plates from the platemaking films. In the printingunit, the printing plates, i.e., four C, M, Y, K printing plates, aremounted on a printing press, and inks of four colors applied to theprinting plates are transferred to a sheet of print paper, which isproduced as a printed hard copy carrying a color image.

The image output unit 35 may comprise a direct digital color proofing(DDCP) system capable of printing halftone dots according to a givenscreen ruling and angle directly as a halftone dot image on a sheet ofprint paper, without developing an image on a film, for simulating theimage.

The colorimetric setup unit 26 comprises a colorimetric ENDforward/inverse conversion matrix (hereinafter also referred to as amatrix) 50 for converting the colorimetric signals 18, 22 intocolorimetric END signals 52, a rough data generator 54 for generatingcolorimetric END signals 56 from the colorimetric END signals 52 througha decimation process and outputting the colorimetric END signals 56, anautomatic setup unit 58 for automatically determining a highlightdensity signal 60 and a shadow density signal 62 based on the generatedcolorimetric END signals 56, and an END/END converter 64 forgradation-converting the colorimetric END signals 52 into colorimetricEND signals 66. The colorimetric END forward/inverse conversion matrix50 also serves to convert the colorimetric END signals 66 back into thecolorimetric signals 22.

The image signal processing apparatus 10 is controlled by a computer(not shown) which includes a CPU, a ROM, a RAM, an external storageunit, a display monitor, an input/output device, and so on. The blocksof the image signal processing apparatus 10 which are described aboveare both hardware-implemented and software-implemented. The computerwhich controls the image signal processing apparatus 10 functions ascontrol, decision, calculation, and comparison means.

Detailed arrangements and operation of the blocks of the image signalprocessing apparatus 10 will be described below.

The matrix 20 of the colorimetric converter 16 for converting R, G, Bsignals 12 into X, Y, Z signals 18 is generated according to a sequenceshown in FIG. 2. The matrix 20 will be described below as convertingsignals from an RGB color space into signals in a CIE-L*a*b* (lightsource: auxiliary standard light CIE-D50) color space. Signal conversionbetween the CIE-L*a*b* color space and the XYZ color space can uniquelybe carried out according to known equations (1) given below. Therefore,processing in the XYZ color space (or the CIE-L*a*b* color space) can bereplaced with processing in the CIE-L*a*b* color space (or the XYZ colorspace), or with processing in an equivalent colorimetric color space.$\begin{matrix}\begin{matrix}{L^{*} = {{116\quad \left( {Y/{Yn}} \right)^{1/3}} - 16}} \\{a^{*} = {500\quad \left\{ {\left( {X/{Xn}} \right)^{1/3} - \left( {Y/{Yn}} \right)^{1/3}} \right\}}} \\{b^{*} = {200\quad \left\{ {\left( {Y/{Yn}} \right)^{1/3} - \left( {Z/{Zn}} \right)^{1/3}} \right\}}}\end{matrix} & (1)\end{matrix}$

First, a color chart 70 having a plurality of color patches 72 (see FIG.2) representing typical colors with different chromas, lightnesses, andhues is generated in a step S1. In this embodiment, a Macbeth colorchecker (registered trademark: manufactured by Macbeth, a division ofKollmorgen, USA) is used as the color chart. As well known in the art,the Macbeth color checker has 24 colored squares with CIE (1931) xyYvalues, hues, Munsell color values, and chromas. The 24 colors are:

1. Dark skin

2. Light skin

3. Blue sky

4. Foliage

5. Blue flower

6. Bluish green

7. Orange

8. Purplish green

9. Moderate red

10. Purple

11. Yellow green

12. Orange yellow

13. Blue

14. Green

15. Red

16. Yellow

17. Magenta

18. Cyan

19. White

20. Neutral 8 (light gray, “8” represents Munsell color value 8)

21. Neutral 6.5 (light medium gray)

22. Neutral 5 (medium gray)

23. Neutral 3.5 (dark gray)

24. Black

The color chart is not limited to the Macbeth color checker, but may bea color chart which uniformly covers a color space, such as JIS standardcolor chips or the like.

Then, under the imaging light source CIE-D50, the 24 colors of the colorchart, i.e., the 24 patches 72, are imaged by a digital camera therebyto produce R, G, B signals 12 of each of the patches 72, and the R, G, Bsignals 12 are converted into luminance values, which are then raised tothe one-third power in a step S2. The R, G, B signals 12 may beconverted into luminance values by canceling the γ correction that hasbeen performed in the device. The luminance values are raised to theone-third power in order to process the produced R, G, B signals 12 inthe CIE-L*a*b* colorimetric system, as can be seen from the aboveequations (1).

Values R, G, B, R², G², B², RG, GB, BR (nine variables) up to terms ofsecond order are calculated from the R, G, B signals 12 produced withrespect to each of the patches 72 in the step S2, specifically, the R,G, B values that have been converged into luminance values and raised tothe one-third power, in a step S3.

Then, the 24-color data of the nine variables produced in the step S3are subjected to a principal component analysis to produce principalcomponent scores V of the nine variables, in order to avoid amulticollineation phenomenon in a multiple regression analysis, in astep S4. For each of the colors, the principal component score V (V isconsidered to be a vector) expressed by the following equation (2):

V=(V ₁ , V ₂ , V ₃ , V ₄ , V ₅ , V ₆ , V ₇ , V ₈ , V ₉)  (2)

In the equation (2), the components (V₁, V₂, V₃, V₄, V ₅, V₆, V₇, V₈,V₉) of the principal component score V are not correlated to each otherwhatsoever, and do not cause the multicollineation phenomenon.

Thereafter, the patches 72 of the color chart 70, i.e., the 24 colors,are measured by a colorimetric meter (not shown) to produce theircolorimetric values C (L*a*b*) (C is also considered to be a vector asit is produced for the 24 colors) in a step S5. The colorimetric valuesC may be measured at any point of time in the steps S2-S4.

Then, using the colorimetric values C as criterion variables (dependentvariables) and the principal component scores V as explanatory variables(independent variables), partial regression coefficients A (A is alsoconsidered to be a vector) are determined according to a multipleregression analysis in a step S6.

The multiple regression analysis is subjected to a weighted matrix(hereinafter referred to as a matrix) P=[Pi] (i=1, 2, . . . 24) that isheld in 1:1 correspondence to the 24 colors of the colorimetric valuesC, which constitute a group of criterion variables, in a step S7. Pirepresents the weight of each color. Of the above 24 colors, the weightsof the grays indicated by 20. Neutral 8 (light gray, “8” representsMunsell color value 8), 21. Neutral 6.5 (light medium gray), 22. Neutral5 (medium gray), and 23. Neutral 3.5 (dark gray) are increased toimprove color reproducibility of the grays. The weights of not only thegrays, but other limited desired colors, e.g., the color of skin forhuman beings or the color of sky for outdoor backgrounds, may beincreased to reproduce those colors of skin and sky with higheraccuracy. The colors may automatically be weighted by statisticallyprocessing the inputted R, G, B signals 12, i.e., dividing the colorspace of a one-frame image signal, i.e., pixel-divided image data, intoregions around the colors of the patches of the chart, and weighting thecolors depending on the frequencies of the pixels present in theregions, e.g., increasing the weight of the color whose pixel frequencyis highest and progressively reducing the weights of the colors whosepixel frequencies are progressively lower. The color space to be dividedmay be an RGB color space or an XYZ (L*a*b*) color space which has beenconverted with an unweighted matrix.

Those colors with increased weights are called principal colors of animage. In order that the sum of the values of elements of the matrix Pis equal to “1”, the values of the elements are standardized by beingdivided by the sum of the values of the elements.

The multiple regression analysis for determining partial regressioncoefficients A in the step S6 will be described in greater detail below.

It is assumed that a linear equation (3) given below is satisfiedbetween the colorimetric values (vector) C which are criterionvariables, the multiple regression coefficients (vector) A to bedetermined, and the principal component scores (vector) V with respectto each of the colorimetric values C of the 24 colors.

C=AV  (3)

The equation (3) is equivalent to an equation (4) expressed by a matrixand equations (5) expressed by a sum symbol Σ as follows:$\begin{matrix}{\begin{pmatrix}L^{*} \\a^{*} \\b^{*}\end{pmatrix} = {\begin{pmatrix}a_{11} & a_{12} & \cdots & a_{110} \\a_{21} & a_{22} & \cdots & a_{210} \\a_{31} & a_{32} & \cdots & a_{310}\end{pmatrix}\begin{pmatrix}V_{1} \\V_{2} \\\vdots \\V_{10}\end{pmatrix}}} & (4) \\{{{where}\quad V_{10}} = 1} & \quad \\\begin{matrix}{L^{*} = {\sum\limits_{j = 1}^{10}\quad {a_{1j}V_{j}}}} \\{a^{*} = {\sum\limits_{j = 1}^{10}\quad {a_{2j}V_{j}}}} \\{b^{*} = {\sum\limits_{j = 1}^{10}\quad {a_{3j}V_{j}}}}\end{matrix} & (5) \\{{{where}\quad V_{10}} = 1} & \quad\end{matrix}$

Equations which use the sum symbol Σ, like the equations (5), will beexpressed as L*=Σ (j=1→10)a_(ij)V_(j), for example.

The partial regression coefficients A are determined using a method ofleast squares with respect to each of L*a*b*. With respect to L*, forexample, partial regression coefficients a_(ij) which minimize e_(L)according to the following equation (6) may be determined:

e _(L)=Σ(i=1→24)Pi{Ci−Σ(j=1→10)a _(1j) V _(j)}²  (6)

where i is the patch number of the color chart 70, Pi is the weight ofeach color, and j is the number (1, 2, . . . , 10) of the variable.

The equation (6) is expressed by a vector and a matrix according to anequation (7) given below. In the equation (7), the colorimetric values Cand the partial regression coefficients a are a vector, and theprincipal component scores [V] and the weighted matrix [P] are a matrix,and t represents the transpose of a matrix.

e _(L)=(vector C−vector a[V])^(t) [P](vectorC−vector a[V])  (7)

Hereinafter, the vector C is indicated simply as C and the vector a asa. The equation (7) can be modified as follows:

 e _(L)=(C ^(t) −[V] ^(t) a ^(t))^(t) [P](C ^(t) −[V] ^(t) a)

By switching a and [V] around, the above equation becomes:

e _(L)=(C−a[V])[P](C ^(t) −[V] ^(t) a)

Since, generally, (ABC)^(t)=C^(t)B^(t)A^(t),

e _(L) =C[P]C ^(t) +a[V][P][V] ^(t) a ^(t) −a[V][P]C ^(t) −C[P][V] ^(t)a ^(t)

By putting [V][P][V]^(t)=[N], [V][P]C^(t)=[U], the above equationbecomes:

e _(L) =C[P]C ^(t) +a[N]a ^(t)−2a[U]  (8)

To minimize e_(L) according to the equation (8), the differential ofeach element of the partial multiple coefficients a must be equal to“0”. Therefore, the following equation is satisfied: $\begin{matrix}{{\frac{1}{2} \cdot \frac{\partial e_{L}}{\partial a}} = {{{\frac{1}{2}\frac{\partial\left( {{a\lbrack N\rbrack}a^{\quad t}} \right)}{\partial a}} - \frac{\partial\left( {a\lbrack U\rbrack} \right)}{\partial a}} = 0}} & (9)\end{matrix}$

From the equation (9), the partial multiple coefficients a can bedetermined according to the following equation (10): $\begin{matrix}{{{\left( {1/2} \right)\quad \left\{ {{\lbrack N\rbrack a^{\quad t}} + \left( {a\lbrack N\rbrack} \right)^{t}} \right\}} - \lbrack U\rbrack} = 0} & (10) \\\begin{matrix}{{\lbrack N\rbrack a^{\quad t}} = \lbrack U\rbrack} \\{a^{\quad t} = {\lbrack N\rbrack^{- 1}\lbrack U\rbrack}} \\{a = {\lbrack U\rbrack^{\quad t}\left( \lbrack N\rbrack^{t} \right)^{- 1}}} \\{= {\left( {{\lbrack V\rbrack \lbrack P\rbrack}C^{t}} \right)^{t}\quad \left( {{\lbrack V\rbrack \lbrack P\rbrack}\lbrack U\rbrack}^{t} \right)^{- 1}}} \\{= {{{C\lbrack P\rbrack}\lbrack U\rbrack}^{t}\quad \left( {{\lbrack V\rbrack \lbrack P\rbrack}\lbrack U\rbrack}^{\quad t} \right)^{- 1}}}\end{matrix} & \quad\end{matrix}$

where [ ]⁻¹ represents an inverse matrix.

In this manner, 10 partial regression coefficients a (a_(1j)) withrespect to L* are determined. Similarly, 10 partial regressioncoefficients a_(2j) with respect to a* and 10 partial regressioncoefficients a_(3j) with respect to b* are determined according to thesame process. The total number of the determined partial regressioncoefficients a_(1j), a_(2j), a_(3j) is 3×10 {see the equation (4)}.

The partial regression coefficients A with respect to the criterionvariables L*a*b* are collectively expressed as follows:

A=CPV ^(t)(VPV ^(t))⁻¹  (11)

The partial regression coefficients A determined with respect to thecriterion variables L*a*b* are then tested with a significance level of5% in a step S8, and explanatory variables V at the significance levelof 5% (confidence level of 95%) are stored in the RAM, and the partialregression coefficients A are stored as the matrix 20 shown in FIG. 1 ina step S9. The partial regression coefficients A may be tested with asignificance level of 1% in the step S8.

In the step S8, as shown in equations (12)-(16) given below, aregression sum S_(R) of squares {a sum of squares of differences betweenthe estimated values L*i (with the symbol “{circumflex over ( )}” over“L”) and the average L*i of the colorimetric values (with the symbol “-”over “L”): see the equation (12)} and a residual sum S_(E) of squares {asum of squares of differences between the colorimetric values L*i andthe estimated values L*i: see the equation (13)} are determined, andunbiased variances V_(R), V_(E) {see the equations (14), (15)} of theregression sum S_(R) of squares and the residual sum S_(E) of squaresare determined. Furthermore, partial F values Fj {see the equation (16)}are determined. In the equation (16), “a” with “{circumflex over ( )}”placed thereover represents estimated values of coefficients of the leftmatrix on the right-hand side of the equation (4), and S^(jj) representsdiagonal terms of inverse matrixes of variance or covariance matrixes ofexplanatory variables v_(j). $\begin{matrix}{S_{R} = {\sum\limits_{i = 1}^{24}\quad \left( {{\hat{L}*i} - {\overset{\_}{L}*i}} \right)^{2}}} & (12) \\{S_{E} = {\sum\limits_{i = 1}^{24}\quad \left( {{L*i} - {\hat{L}*i}} \right)^{2}}} & (13) \\{V_{R} = \frac{S_{R}}{10}} & (14) \\{V_{E} = \frac{S_{E}}{24 - 9 - 1}} & (15) \\{F_{j} = \frac{{\hat{a}}_{j}^{2}}{S^{jj}{V_{E}/\left( {24 - 1} \right)}}} & (16)\end{matrix}$

If the partial F values Fj are greater than a value=F′_(n−p−1)(0.05)=F′²⁴⁻⁹⁻¹ (0.05)=F′₁₄ (0.05)=4.60011 which has been determined byreferring to an F distribution with a signification level of a 5%, thenthe regression is significant with the signification level of 5%, andthe partial regression coefficients are judged as useful for predictionand stored in the matrix 20 (see FIG. 1).

In this manner, it is possible to generate the matrix 20 for determiningthe criterion variables L*a*b* which will convert the R, G, B signals(see FIG. 1) produced by the digital camera into the X, Y, Zcolorimetric signals 18. Equations relative to the matrix are givenbelow: $\begin{matrix}{{L^{*} = {\sum{a_{1j}{v_{j}\left( {j:{a\quad {variable}\quad {at}\quad a\quad {significance}\quad {level}\quad {of}\quad 5\quad \%}} \right)}}}}{a^{*} = {\sum{a_{2j}{v_{j}\left( {j:{a\quad {variable}\quad {at}\quad a\quad {significance}\quad {level}\quad {of}\quad 5\quad \%}} \right)}}}}{b^{*} = {\sum{a_{3j}{v_{j}\left( {j:{a\quad {variable}\quad {at}\quad a\quad {significance}\quad {level}\quad {of}\quad 5\quad \%}} \right)}}}}} & (17)\end{matrix}$

In the equations (17), the remark (j: a variable with a significancelevel of 5%) means that only a variable obtained by the test with thesignificance level of 5% is used, unlike the equation (5). In theequations (17), a_(1j), a_(2j), a_(3j) represent elements of the partialregression coefficient matrix A, and v_(j) represent the principalcomponent scores V as the explanatory variables determined in the stepS4.

Using the matrix 20, it is possible to effect colorimetric conversionwith the existing color chart 70. Since no lookup table is employed, anymemory capacity used may be small, i.e., colorimetric conversion can beeffected with accuracy even though the memory capacity used is small.Use of the weighted matrix P makes it possible to effect colorconversion with accuracy, limited to desired colors (principal colors)of the image. If the number of explanatory variables is 9, then thetesting process in the step S8 may be dispensed with, and a matrixcorresponding to the equations (17) may be generated using all thepartial regression coefficients A.

The values thus obtained in the CIE-L*a*b* color space are convertedinto values in the XYZ color space according to the equations (1), whichare used as the colorimetric signals 18 outputted from the matrix 20(see FIG. 1).

The colorimetric signals 18 are then colorimetrically changed by thelight source changer 25 into new colorimetric signals 18. If theobserving light source is the same as the imaging light source (CIE-D50)used when the image is captured by the digital camera, then colorimetricsignals 18 do not need to be colorimetrically changed by the lightsource changer 25.

Colorimetric values X, Y, Z can be converted into new colorimetricvalues X′, Y′, Z′ by the light source changer 25 according to thefollowing equations (18), (19), (20): $\begin{matrix}{X^{\prime} = {{XX}_{2}/X_{1}}} & (18) \\{Y^{\prime} = {{YY}_{2}/Y_{1}}} & (19) \\{Z^{\prime} = {{ZZ}_{2}/Z_{1}}} & (20)\end{matrix}$

where X, Y, Z represent colorimetric values under the imaging lightsource, X′, Y′, Z colorimetric values under the observing light source,X₁, Y₁, Z₁ white dots of the imaging light source, and X₂, Y₂, Z₂ whitedots of the observing light source.

It is hereinafter assumed that the colorimetric signals 18 have beenconverted by the light source changer 25.

The R, G, B signals from the scanner input unit are converted intocolorimetric signals 22 which are X, Y, Z signals by the colorimetricconversion table 24.

The colorimetric conversion table 24 is prepared as follows: First, acolor reversal subject having, for example, a combination of 13 equallyspaced densities of c, m, y color patches, i.e., a total of13×13×13=2197 color patches, is prepared. Then, the color patches of thereversal subject are read by a scanner input unit and also read by acolorimetric meter. The R, G, B values read by the scanner input unitand the X, Y, Z values read by the colorimetric meter are associatedwith each other into a table, which will be used as the colorimetricconversion table 24. Values between the read values, which are notpresent in the colorimetric conversion table 24, are determined by aninterpolation process.

Since the R, G, B signals 14 supplied from the scanner input unit areconverted into the colorimetric signals 22 by the colorimetricconversion table 24 and the R, G, B signals 12 supplied from the digitalcamera are converted into the colorimetric signals 18 by thecolorimetric conversion matrix 20, they can be processed by an automaticsetup process using the colorimetric setup unit 26 in common.Specifically, automatic setup process software can be used in common onthe colorimetric signals 18, 22 because the colorimetric signals 18, 22are converted into a colorimetric END signal.

The colorimetric setup unit 26 and its operation sequence will bedescribed below with reference to FIGS. 1, 3, and 4.

Heretofore, gradation conversion has been carried out in a density spaceas its intuitive understanding can easily be attained in the densityspace. Image processing such as gradation conversion and colorcorrection on commercially available color scanners is also effectedwith respect to density signals.

First, the colorimetric END forward/inverse conversion matrix 50converts the colorimetric signals 18 or the colorimetric signals 22 intoc, m, y END (equivalent neutral density) signals 52, which arecolorimetric density signals, in a step S11. Specifically, theconversion process in the step S11 comprises a linear conversion processfor converting colorimetric signals in an XYZ colorimetric system intocolorimetric signals in an RGB colorimetric system in a step S11 a, anda nonlinear conversion process for converting colorimetric signals inthe RGB colorimetric system into colorimetric signals in a cmycolorimetric system in a step S11 b. The END signals converted from thecolorimetric signals 18 or the colorimetric signals 22 are referred toas colorimetric END signals 52.

For converting the colorimetric signals 22 of the reversal subject intothe colorimetric END signals 52, for example, a color reproduction range71 (shown hatched in FIG. 4) of a reversal film is drawn on a CIEchromaticity diagram shown in FIG. 4. It is assumed that coordinates onthe chromaticity diagram, i.e., chromaticity coordinates, of threereference color stimuli Rxyz, Gxyz, Bxyz of a range 73 which containsthe color reproduction range 71 are represented by equations (21)-(23)given below. The range 73 represents a triangular area whose vertexesare indicated respectively by the reference color stimuli Rxyz, Gxyz,Bxyz on the chromaticity diagram shown in FIG. 4. $\begin{matrix}{{Rxyz} = {{Rxyz}\quad \left( {x_{R},y_{R},z_{R}} \right)}} & (21) \\{{Gxyz} = {{Gxyz}\quad \left( {x_{G},y_{G},z_{G}} \right)}} & (22) \\{{Bxyz} = {{Bxyz}\quad \left( {x_{B},y_{B},z_{B}} \right)}} & (23)\end{matrix}$

Coordinates of a basic stimulus (white stimulus) Wxyz in the XYZcolorimetric system on the chromaticity diagram shown in FIG. 4 areexpressed as follows:

 Wxyz=Wxyz(x _(W) , Y _(W) , z _(W))  (24)

The equation (25) given below can convert the colorimetric signals 22 inthe XYZ colorimetric system (the right matrix on the right-hand side ofthe equation (25)) through a conversion matrix (the left matrix on theright-hand side of the equation (25)) into color signals R, G, B in theRGB colorimetric system (the matrix on the left-hand side of theequation (25)). $\begin{matrix}{\begin{pmatrix}R \\G \\B\end{pmatrix} = {\begin{pmatrix}R_{X} & R_{Y} & R_{Z} \\G_{X} & G_{Y} & G_{Z} \\B_{X} & B_{Y} & B_{Z}\end{pmatrix}\begin{pmatrix}X \\Y \\Z\end{pmatrix}}} & (25)\end{matrix}$

where R_(X) = A₁₁/Δ₁, R_(Y) = A₁₂/Δ₁, R_(Z) = A₁₃/Δ₁G_(X) = A₂₁/Δ₂, G_(Y) = A₂₂/Δ₂, G_(Z) = A₃₃/Δ₂B_(X) = A₃₁/Δ₃, B_(Y) = A₃₂/Δ₃, B_(Z) = A₃₃/Δ₃Δ_(i) = A_(i1)x_(W) + A_(i2)y_(W) + A_(i3)Z_(W)

A_(ij) represents a cofactor of the following matrix equation D:$D = {\begin{matrix}x_{R} & y_{R} & z_{R} \\x_{G} & y_{G} & z_{G} \\x_{B} & y_{B} & z_{B}\end{matrix}}$

The cofactor A_(ij) is determined according to the following equation:

A _(ij)=(−1)^(i+j) D _(ij)

where D_(ij) represents a submatrix from which an ith row and a jthcolumn have been removed.

The c, m, y values of the colorimetric END signals 52 can be determinedby finding ratios of the values of R, G, B calculated according to theequation (25) to the values Rw, Gw, Bw of R, G, B which are calculatedby substituting the coordinates of the basic stimulus (white stimulus)Wxyz in the equation (25), and logarithmically converting the ratiosaccording to the following equations (26) through (28): $\begin{matrix}{c = {{- \quad \log}\quad \left( {R/{Rw}} \right)}} & (26) \\{m = {{- \quad \log}\quad \left( {G/{Gw}} \right)}} & (27) \\{y = {{- \quad \log}\quad \left( {B/{Bw}} \right)}} & (28)\end{matrix}$

The rough data generator 54 effects a rough a scanning process in orderto shorten the setup process by selecting data from only an image areaof the subject that has been specified by the operator through the CPUor generating decimated data from an image that is present on the entirearea of the subject, rather than processing colorimetric END signals 52of the entire subject.

Then, the automatic setup unit 58 carries out an automatic setup processbased on colorimetric END signals 56 which are the rough data selectedby the rough data generator 54 in a step S12. In the automatic setupprocess, the automatic setup unit 58 generates a histogram with respectto the colorimetric END signals 56 and thereafter generates a cumulativehistogram, as is known from Japanese laid-open patent publication No.2-105676.

As indicated by a characteristic diagram representative of operation ofthe END/END converter 64 in FIG. 1, a value of the colorimetric ENDsignals 56 which corresponds to 0.1% point data (HL density) D1 is setto DH, and a value of the colorimetric END signals 56 which correspondsto 98% point data (SD density) D2 is set to DS, in the step S12.

If the density of D1 is D1=1.0 and the density of D2 is D2=2.0, forexample, DH corresponding to the density of D1 is set to DH=0.1 and DScorresponding to the density of D2 is set to DS=3.0. Actually, pointdata (HL density) D1 of 0.1% is converted into a density correspondingto 0% in terms of halftone dot %, and point data (SD density) D2 of 98%is converted into a density corresponding to 100% in terms of halftonedot %.

Using the equation of a straight line 74 thus established in thecharacteristic diagram, the END/END converter 64 converts all thecolorimetric END signals 52 of main scanned data (data toward which therough scanning process is directed) into colorimetric END signals 66,i.e., converts gradation characteristics or corrects the gradation ofthe colorimetric END signals 52. The conversion equation used by theEND/END converter 64 may be the equation of a curve that passes througha highlight point 75 (D1, DH) and a shadow point 76 (D2, DS). In theautomatic setup process, the values of HL density D1 and SD density D2(or the setup points 75, 76) are automatically determined under thepredetermined conditions described above. However, the values of HLdensity D1 and SD density D2 may be determined manually, or the valuesof DH, DS may be corrected manually after the automatic setup process,in a manual setup process.

Then, the colorimetric END forward/inverse conversion matrix inverselyconverts the c, m, y values of the colorimetric END signals 66 convertedby the END/END converter 64 back into X, Y, Z values of colorimetricsignals 22 in a step S14. Specifically, the conversion process in thestep S14 comprises a conversion process for converting the c, m, yvalues of the colorimetric END signals 66 into R, G, B values in an RGBcolorimetric system in a step S14 a, and a conversion process forconverting the R, G, B values in the RGB colorimetric system intocolorimetric signals 22 which have X, Y, Z values in an XYZ colorimetricsystem in a step S14 b. The conversion process for converting the c, m,y values of the colorimetric END signals 66 into R, G, B values in anRGB colorimetric system is carried out according to the followingequations (29)-(31) which solve the equations (26)-(28) for R, G, B, andthe conversion process for converting the R, G, B values in the RGBcolorimetric system into X, Y, Z values in an XYZ colorimetric system iscarried out according to the following equation (32) which solves theequation (25) for a matrix XYZ: $\begin{matrix}{R = {Rw10}^{- c}} & (29) \\{G = {Gw10}^{- m}} & (30) \\{B = {Bw10}^{- y}} & (31) \\{\begin{pmatrix}X \\Y \\Z\end{pmatrix} = {\begin{pmatrix}R_{X} & R_{Y} & R_{Z} \\G_{X} & G_{Y} & G_{Z} \\B_{X} & B_{Y} & B_{Z}\end{pmatrix}^{- 1}\begin{pmatrix}R \\G \\B\end{pmatrix}}} & (32)\end{matrix}$

The above setup process has been described with respect to colorimetricsignals 22 from a reversal subject. However, the setup process may beapplied to colorimetric signals 18 from a digital camera.

Specifically, reversal subjects are generally used as original subjectsin the art of printing and platemaking. A reversal subject is exposed toan original scene, and a print is produced on the basis of colors in acolor reproduction range 71 on the reversal subject. This means that thecolor reproduction range 71 in which colors are produced on the reversalsubject is sufficient as a color reproduction range for image signalsthat are handled.

As described above, the colorimetric setup unit 26 linearly convertscolorimetric values X, Y, Z into R, G, B values in a three-primarycolorimetric system, logarithmically converts the ratios of the R, G, Bvalues to basic stimuli (light source) Rw, Gw, Bw thereby to determineEND values, and effects the automatic setup process using the ENDvalues. Then, the colorimetric setup unit 26 converts the END valuesinto other END values, inversely logarithmically converts the END valuesinto modified R, G, B values, and converts the R, G, B values intocolorimetric values X, Y, Z. Therefore, the automatic setup process canbe carried out on the basis of existing density signals. The setupprocess may also be effected as the manual setup process.

The dye density converter 30 will be described below with reference toFIGS. 1, 5, and 6.

The dye density converter 30 converts the colorimetric signals (X, Y, Z)18 of an original scene which are captured by a digital camera into dyedensity signals (c, m, y) 28 on a reversal subject.

The conversion process carried out by the dye density converter 30 maybe a process shown in FIG. 5 or a process shown in FIG. 6.

According to the process shown in FIG. 5, the colorimetric signals (X,Y, Z) 18 of an original scene are converted into dye density signals (c,m, y) 81 of the original scene by a lookup table in a step S21 (firststage), and the dye density signals (c, m, y) 81 of the original sceneare converted into dye density signals (c, m, y) 28 on a reversalsubject (hereafter also referred to as an RV) by a matrix (describedlater on) in a step S22 (second stage). According to the process shownin FIG. 6, the colorimetric signals (X, Y, Z) 18 of an original sceneare converted into dye density signals (c, m, y) 82 on a reversalsubject by a matrix (described later on) in a step S31 (first stage),and the dye density signals (c, m, y) 82 on the reversal subject areconverted into dye density signals (c, m, y) 28 on the reversal subjectby a lookup table in a step S32 (second stage).

The process shown in FIG. 5 will be described in detail with referenceto FIG. 7.

Calculations in the step S21 for determining dye densities c, m, y fromcolorimetric values (tristimulus values) X, Y, Z will be describedbelow.

The following equations (33)-(36) are satisfied between the colorimetricvalues X, Y, Z and the dye densities c, m, y: $\begin{matrix}{X = {k{\int{{{visP}(\lambda)}{T(\lambda)}{x(\lambda)}{\lambda}}}}} & (33) \\{Y = {k{\int{{{visP}(\lambda)}{T(\lambda)}{y(\lambda)}{\lambda}}}}} & (34) \\{Z = {k{\int{{{visP}(\lambda)}{T(\lambda)}{z(\lambda)}{\lambda}}}}} & (35) \\{{T(\lambda)} = 10^{- h}} & (36)\end{matrix}$

where

h={cDc(λ)+mDm(λ)+yDy(λ)+base(λ)}

k=100/∫visP(λ)y(λ)dλ (λ represents the wavelength of light)

∫vis: definite integral in the visible wavelength range (380 nm-780 nm)

P(λ): spectral data of the observing light source

T(λ): spectral transmittance data of the dye of a transmissive object

x(λ), y(λ), z(λ): color matching function

Dc(λ), Dm(λ), Dy(λ): spectral density data of c, m, y dyes

base(λ): spectral density data of a film base

To determine the dye densities c, m, y from the equations (33)-(36),inverse functions may be determined. However, such inverse functionscannot directly be determined. One solution is to use a successiveapproximating process such as the known Newton-Raphson method (see, forexample, “Color Engineering” written by Noboru Ohta, pp. 254-260,published by Tokyo Denki University, publishing office, Dec. 20, 1993,1st edition, 1st print) The Newton-Raphson method will briefly bedescribed with reference to the above book.

When a general equation y=f(x) is expanded into a Taylor's series withx=x0 close to the root of f(x)=0, and only a term of first order istaken, f(x0+Δx)=f(x0)+f′(x0)·Δx is satisfied with respect to a minutechange Δx of x. f′(x0) is produced by substituting x=x0 in adifferential coefficient f′(x) of f(x). Therefore, a more correct valuex1 of f(x)=0 is determined, assuming f(x0+Δx)=0, fromx1=x0+Δx=x0−f(x0)/f′(x0). As shown in FIG. 8, this is equivalent todrawing a tangential line 83 to a point (x0, y0) on a function y=f(x)and determining a point x1 of intersection between the tangential line83 and the x-axis.

Substituting the equation (36) in the equations (33)-(35) and usingcertain functions fx, fy, fz, the equations (33)-(35) can be expressedas the following equations (37)-(39): $\begin{matrix}{X = {{fx}\left( {c,m,y} \right)}} & (37) \\{Y = {{fy}\left( {c,m,y} \right)}} & (38) \\{Z = {{fz}\left( {c,m,y} \right)}} & (39)\end{matrix}$

It is assumed in the equations (37)-(39) that initial values arerepresented by c0, m0, y0 and tristimulus values by X0, Y0, Z0 at thoseinitial values. If the tristimulus values X0, Y0, Z0 change respectivelyby ΔX0, ΔY0, ΔZ0 when the initial values c0, m0, y0 are subjected tominute changes Δc0, Δm0, Δy0, then the following equation (40) isobtained: $\begin{matrix}\begin{matrix}{{{X0} + {\Delta \quad X}} = \quad {{fx}\left( {{{c0} + {\Delta \quad c}},{{m0} + {\Delta \quad m}},{{y0} + {\Delta \quad y}}} \right)}} \\{= \quad {{{fx}\left( {{c0},{m0},{y0}} \right)} + {\Delta \quad {c \cdot {{\partial{fx}}/{\partial\quad c}}}} +}} \\{\quad {{\Delta \quad {m \cdot {{\partial{fx}}/{\partial m}}}} + {\Delta \quad {y \cdot {{\partial{fx}}/{\partial y}}}}}} \\{= \quad {{X0} + {\Delta \quad {c \cdot {{\partial X}/{\partial c}}}} + {\Delta \quad {m \cdot {{\partial X}/{\partial m}}}} + {\Delta \quad {y \cdot {{\partial X}/{\partial y}}}}}}\end{matrix} & (40)\end{matrix}$

where ∂fx/∂c, for example, represents a partial differential coefficientof the function fx with respect to c. The equation (40) is modified intoan equation (41) given below. Similarly, ΔY, ΔZ are also represented byrespective equations (42), (43) as follows: $\begin{matrix}{{\Delta \quad X} = {{\Delta \quad {c \cdot {{\partial X}/{\partial c}}}} + {\Delta \quad {m \cdot {{\partial X}/{\partial m}}}} + {\Delta \quad {y \cdot {{\partial X}/{\partial y}}}}}} & (41) \\{{\Delta \quad Y} = {{\Delta \quad {c \cdot {{\partial Y}/{\partial c}}}} + {\Delta \quad {m \cdot {{\partial Y}/{\partial m}}}} + {\Delta \quad {y \cdot {{\partial Y}/{\partial y}}}}}} & (42) \\{{\Delta \quad Z} = {{\Delta \quad {c \cdot {{\partial Z}/{\partial c}}}} + {\Delta \quad {m \cdot {{\partial Z}/{\partial m}}}} + {\Delta \quad {y \cdot {{\partial Z}/{\partial y}}}}}} & (43)\end{matrix}$

The equations (41)-(43) can be presented in a matrix representationaccording to the following equation (44):

(Q)=(J)(P)  (44)

where (Q) represents a three-row, one-column matrix of elements whichare ΔX, ΔY, ΔZ arranged successively from the first row, (J) a Jacobianmatrix of partial differential coefficients arranged in three rows andthree columns, and (P) a three-row, one-column matrix of elements whichare Δc, Δm, Δy arranged successively from the first row.

When both sides of the equation (44) are multiplied by an inverse matrix(J)⁻¹ of the Jacobian matrix (J), the following equation (45) isobtained:

(P)=(J)⁻¹(Q)  (45)

Therefore, if the initial values c0, m0, y0 are corrected respectivelyinto c1, m1, y1 as indicated by the following equations (46), then morecorrect approximate values can be produced: $\begin{matrix}{{{c1} = {{c0} + {\Delta \quad c}}}{{m1} = {{m0} + {\Delta \quad m}}}{{y1} = {{y0} + {\Delta \quad y}}}} & (46)\end{matrix}$

By repeating calculations using the Jacobian matrix (J) thus obtained,it is possible to determine dye density signals c, m, y with respect tocolorimetric values X, Y, Z which are arbitrary target values. The sameprocess is effected on all target values on a grid in the XYZ colorspace to generate an inverse conversion table for converting thecolorimetric signals (X, Y, Z) 18 of the original scene into dye densitysignals (c, m, y) 81 of the original scene, and the inverse conversiontable is stored as a lookup table for the first-stage processing in thedye density converter 30 in the step S21.

A process of generating a matrix for use in the second stage whichfollows the first stage in the step S21 will be described below. Theprocess of generating a matrix for use in the second stage includes themultiple regression analysis described above with reference to FIG. 2,and hence will briefly be described below.

With respect to the dye densities c, m, y calculated in the step S21,data of 24 colors of nine variables c, m, y, c², m², y², cm, my, yc arecalculated in a step S22 a.

Then, the 24-color data of the nine variables produced in the step S22 aare subjected to a principal component analysis to produce principalcomponent scores of the nine principal components in a step S22 b.

Colorimetric values X, Y, Z of 24 colors of a Macbeth color checker on areversal film are measured by a colorimetric meter in a step S22 c.

Dye densities c, m, y (also referred to as RVc, RVm, RVy) on thereversal film are determined by the Newton-Raphson method in a step S22d.

With each of the dye densities RVc, RVm, RVy being used as a criterionvariable and also with the principal component scores (includingconstant terms) determined in the step S22 b being used as explanatoryvariables, a 3×10 matrix of partial regression coefficients isdetermined by a multiple regression analysis in a step S22 e.

The multiple regression analysis may be subjected to a weighted matrixthat is held in 1:1 correspondence to the 24 colors which constitute agroup of criterion variables in a step S22 f.

Then, the partial regression coefficients determined with respect to thecriterion variables RVc, RVm, RVy are tested with a significance levelof 5% in a step S22 g, and explanatory variables at the significancelevel of 5% are stored in the RAM, and the partial regressioncoefficients are stored as the matrix for use in the second-stageprocessing in the dye density converter 30 in a step S22 h. The partialregression coefficients may not be tested, but all of them may be used.

In this manner, the dye density converter 30 for converting colorimetricsignals 18 of an original scene into dye density signals c, m, y on areversal subject is constructed.

The process shown in FIG. 6 will be described with reference to FIG. 9.The sequence shown in FIG. 9 includes the multiple regression analysisdescribed above with reference to FIG. 2, and hence will briefly bedescribed below.

Colorimetric values X, Y, Z of the 24 colors of the Macbeth colorchecker 70 are measured by a colorimetric meter in a step S31 a (FIG.9).

With respect to the measured colorimetric data X, Y, Z, data of 24colors of nine variables X, Y, Z, X², Y², Z²,XY, YZ, ZX are calculatedin a step S31 b.

Then, the 24-color data of the nine variables are subjected to aprincipal component analysis to produce principal component scores ofthe nine principal components in a step S31 c.

Colorimetric values X, Y, Z of 24 colors of a Macbeth color checker on areversal film are measured by a colorimetric meter in a step S31 d. TheMacbeth color checker on the reversal film is produced by photographingthe image of a Macbeth color checker under a certain light source withan optical camera loaded with a reversal film, and then developing thephotographed image on the reversal film.

With the colorimetric values X, Y, Z being used as criterion variables(RVX, RVY, RVZ) and also with the principal component scores (includingconstant terms) determined in the step S31 c being used as explanatoryvariables, a 3×10 matrix of partial regression coefficients isdetermined by a multiple regression analysis in a step S31 e.

The multiple regression analysis may be subjected to a weighted matrixthat is held in 1:1 correspondence to the 24 colors which constitute agroup of criterion variables in a step S31 f.

Then, the partial regression coefficients determined with respect to thecriterion variables RVX, RVY, RVZ are tested with a significance levelof 5% in a step S31 g, and explanatory variables at the significancelevel of 5% are stored in the RAM, and the partial regressioncoefficients are stored as the matrix for use in the first-stageprocessing in the dye density converter 30 in a step S31 h. The partialregression coefficients may not be tested, but all of them may be used.

The table for use in the second stage in the step S32 is generatedaccording to a process which is the same as the process in the step S21or the step S22 d described with reference to FIG. 7, and hence theprocess of generating the table for use in the second stage in the stepS32 will not be described below.

A process of generating the accurate original scene reproduction table42 of the cmyk converter 32 will be described below with reference toFIG. 10.

The accurate original scene reproduction table 42 is a lookup table forconverting the colorimetric signals (X, Y, Z) 18 into c, m, y, k signals(c, m, y, k data) 46 which are colorimetrically saved halftone dot %data.

The process of generating the accurate original scene reproduction table42 is the same as a process described in Japanese patent application No.8-154584 filed by the same applicant as the present application.

A plurality of c, m, y, k halftone dot % data spaced at regularintervals are supplied to the image output unit 35 to generate a cmykcolor chart having color patches whose c, m, y, k densities and mixedratios vary stepwise in a step S41.

The regular intervals may be six intervals comprising increments of 20%,representing 0, 20, . . . , 100% in terms of halftone dot % for each ofthe colors c, m, y, k. In such a case, the total number of color patchesis 4⁶=1296.

Then, the color patches of the cmyk color chart generated by the imageoutput unit 35 are colorimetrically measured by a colorimetric meter ina step S42. Colorimetric values (stimulus values) X, Y, Z are determinedfrom the measured colorimetric data, and a conversion table (called aforward conversion table) for converting the c, m, y, k halftone dot %data into colorimetric value data X, Y, Z is generated in a step S43.

The forward conversion table is used as an interpolating table.Therefore, the smaller the regular intervals described above, the betterfor interpolation accuracy. However, since smaller regular intervalsresult in a larger expenditure of time and labor required forcolorimetrically measuring the color patches, a trade-off should be madebetween the complexity of the colorimetric measurement and theinterpolation accuracy, and a computer processing time described below.

For determining c, m, y, k signals (c, m, y, k halftone dot % data,color data c, m, y, k, or c, m, y, k) from inputted arbitrarycolorimetric value signals (colorimetric value data X, Y, Z, stimulusvalue data X, Y, Z, or X, Y. Z) according to the accurate original scenereproduction table 42, since variables increase from three variables tofour variables, a plurality of solutions of c, m, y, k halftone dot %data 46 may exist with respect to one set of colorimetric value data X,Y, Z. In order to eliminate this drawback, it is necessary to establisha relationship between three variables and three variables for theaccurate original scene reproduction table 42. To meet this requirement,the color data k of the c, m, y, k halftone dot % data is fixed to amaximum value Kmax (k=Kmax) that can be employed by the image outputunit 35 in a step S44. The maximum value Kmax is the same as 100%, forexample, for the value of k of the c, m, y, k halftone dot % data.

Then, arbitrary values X, Y, Z which are three variables are convertedinto corresponding values c, m, y (with k being fixed) which are threevariables in a step S45.

For determining c0, m0, y0, k0=Kmax which are values c, m, y, k (since kis fixed to k=Kmax, k is a constant, and therefore c, m, y are threevariables) with respect to target values X0, Y0, Z0 that are arbitraryvalues X, Y, Z, the forward conversion table with k0=Kmax is used todetermine partial regression coefficients of a regression equation.

If it is assumed that a matrix of coefficient terms in three rows andfour columns is represented by A, a matrix of colorimetric values X, Y,Z in three rows and one column by T, and a matrix of c, m, y, k (k isfixed or a constant) in four rows and one column by D, then theregression equation is expressed by:

T=AD  (47)

The equation (47) represents the following equation (48):$\begin{matrix}{\begin{pmatrix}X \\Y \\Z\end{pmatrix} = {\begin{pmatrix}A_{X1} & A_{X2} & A_{X_{3}} & A_{X4} \\A_{Y1} & A_{Y2} & A_{Y3} & A_{Y4} \\A_{Z1} & A_{Z2} & A_{Z3} & A_{Z4}\end{pmatrix}\begin{pmatrix}c \\m \\y \\1\end{pmatrix}}} & (48)\end{matrix}$

In the equations (47), (48), “1” of the matrix D is a value establishedto give a constant term in a three-dimensional plane equation of c, m,y.

The coefficients in the equation (48) can be determined according to theabove multiple regression analysis by substituting the data sets of theabove forward conversion table which are obtained when k=Kmax.

Then, using the regression equation determined when k=Kmax, c, m, y(with k being established as Kmax) values corresponding to the targetvalues X0, Y0, Z0 can be determined according to the Newton-Raphsonmethod.

Therefore, it is determined whether the determined c, m, y values arevalues (colors) within a reproduction range of the image output unit 35in a step S46. Specifically, it is determined whether the determinedvalues c, m, y satisfy the following ranges: $\begin{matrix}{{{Cmin} \leq c \leq {Cmax}}{{Mmin} \leq m \leq {Mmax}}{{Ymin} \leq y \leq {Ymax}}} & (49)\end{matrix}$

where Cmin, Mmin, Ymin represent reproducible minimum densities of thehalftone dot % data (color data) and Cmax, Mmax, Ymax reproduciblemaximum densities thereof.

If the determined values c, m, y satisfy the ranges (49), then aninverse conversion table is generated by setting the color data c, m, y,k with respect to the target values (X0, Y0, Z0) for the stimulus valuedata X, Y, Z respectively to c=csol, m=msol, y=ysol, k=ksol (ksol=Kmax),and established as the accurate original scene reproduction table 42 ina step S47. The data set (csol, msol, ysol, ksol) will also be referredto as color data cmyksol.

If the determined values c, m, y do not satisfy the ranges (49), thenthe color data k which has been fixed to k=Kmax is set to k=k−Δk, i.e.,k=kmax−Δk, in a step S48, and the step S45 is repeated insofar as thecolor data k is greater than a given minimum value k=Kmin in a step S49.The minute change Δk constitutes an arbitrary data interval of the colordata k of the inverse conversion table. For example, if the color data kis established as data in a range from 0% to 100%, then the minutechange Δk may be set to 1%, and if the color data k is established asdata in a range from 0 to 255, then the minute change Δk may be setto 1. In the second execution of the step S45, the values X, Y, Z whichare colorimetric vales on the left-hand side of the equation (48) withrespect to the color data k=Kmax =1=100−1 =99 can be determined byinterpolating the X, Y, Z data colorimetrically measured at the valuesk=Kmax=100% and k=80% and stored in the forward conversion table.

If Kmin>k in the step S49, then the color data c, m, y, k with respectto the target values (X0, Y0, Z0) are indicated as data outside of thecolor reproduction range of the image output unit 35, and no color datacmyksol is calculated in a step S50.

The above process is carried out while all the stimulus value data X, Y,Z 18 supplied to the accurate original scene reproduction table 42 arebeing used as target values (X0, Y0, Z0), for thereby determining colordata cmyksol when maximum color data k is given to the stimulus valuedata X, Y, Z 18 capable of producing halftone dot % data c, m, y, k 46within the color reproduction range of the image output unit 35, in astep S51.

In the step S44, the color data k may be fixed to the minimum value Kmin(k=Kmin), rather than the maximum value Kmax. In such a case, the colordata k is set to k=k+Δk in the step S48, and it is determined in thestep S49 whether the color data k is greater than the maximum value Kmax(Kmax<k). The color data k may alternatively be set to an arbitraryvalue. If the color data k is set to an arbitrary value, then the colordata k may set alternately to k=k−Δk and k=k+Δk, and the step S49 may becarried out in a manner depending on the step S48.

The color data c, m, y, k indicated as data outside of the colorreproduction range of the image output unit 35 in the steps S49, S50will not be described in detail because they have no direct bearing onthe features of the present invention. However, as described in Japanesepatent application No. 8-154584, it is possible to generate an inverseconversion table of color data c, m, y, k with respect to the targetvalues (X0, Y0, Z0) based on the compression or clipping of color dataC, M, Y, K according to gamut mapping.

The association table of the c, m, y, k signals 46 determined withrespect to all the target values (X0, Y0, Z0) of the colorimetricsignals 18 is stored as the accurate original scene reproduction table42 for thereby converting arbitrary colorimetric signals 18 into c, m,y, k signals 46 within the color reproduction range of the image outputunit 35.

A process of generating the standard condition reproduction table 44 ofthe cmyk converter 32 will be described below.

As shown in FIG. 1, the R, G, B signals 12 captured by the digitalcamera are converted by the matrix 20 into the colorimetric signals (X,Y, Z) 18, which are then converted by the dye density converter 30 intothe dye density signal (c, m, y) 28. The standard condition reproductiontable 44 serves to convert the dye density signal (c, m, y) 28 into c,m, y, k signals 48 which are halftone dot % signals.

As shown in FIG. 1, furthermore, the R, G, B signals 14 captured by thescanner are converter by the table 24 into the colorimetric signals (X,Y, Z) 22, which are then converted by the dye density converter 36 intothe dye density signal (c, m, y) 34. The standard condition reproductiontable 44 also serves to convert the dye density signal (c, m, y) 34 intoc, m, y, k signals 48 which are halftone dot % signals. The dye densityconverter 36 can be generated in the same manner as the processes in thesteps S21, S22 d shown in FIG. 7 for the generation of the dye densityconverter 30, and hence will not be described in detail.

The process of generating the standard condition reproduction table 44will be described below with reference to FIG. 11.

First, a color chart having 13×13×13 color patches having different dyedensities c, m, y that are arranged in a grid-like pattern on a reversalsubject are generated in a step S61. Specifically, the color chart hascolor patches representing a combination of colors c, m, y, each having13 densities ranging from a minimum density to a maximum density.

Then, the color chart is color-separated under color-separating defaultconditions of a color scanner. Stated otherwise, the color chart, whichis a transmissive subject, is read by the color scanner so as to beconverted into digital data in a step S62. The color-separating defaultconditions include at least a gradation conversion process, a colorcorrection process, an under color removal process, and a K-plategenerating process. For color separation, the minimum density of gray ofthe color patches is established such that the corresponding data of c,m. y, k halftone dot % are all 0%, and the maximum density of gray ofthe color patches is established such that the corresponding data of c,m, y, k halftone dot % represent a solid image.

Then, the dye densities of the color patches and the values of c, m, y,k halftone dot % which are color-separated under the color-separatingdefault conditions of the color scanner are associated with each other,thus generating a conversion table (lookup table) as the standardcondition reproduction table 44 in a step S63. Actually, arbitrary dyedensity signals (c, m, y) 28 or dye density signals (c, m, y) 34 rangingfrom the minimum density to the maximum density can be converted intodesired c, m, y, k halftone dot % signal 48 by the standard conditionreproduction table 44 and an interpolation process.

As described above, according to the embodiment of the presentinvention, for converting arbitrary color signals 12 in a first colorspace, e.g., R, G, B signals 12 captured by a digital camera, into colorsignals in a second color space, e.g., colorimetric signals (X, Y, Z orL*, a*, b*) 18, a color chart having a plurality of color patchesrepresenting typical colors with different chromas, lightnesses, andhues, i.e., a commercially available Macbeth color checker, is imaged bythe digital camera which is to output the color signals 12 in the firstcolor space. At least first-order, second-order, and cross terms of R,G, B signals of the imaged color chart are calculated as a group offirst explanatory variables. The group of first explanatory variables isthen subjected to a principal component analysis for conversion into agroup of second explanatory variables which cross each other.Colorimetric values of the color patches of the color chart (colorsignals in the second color space) are used as a group of criterionvariables, and the group of criterion variables and the group of secondexplanatory variables are subjected to a multiple regression analysis todetermine a matrix of partial regression coefficients. Finally, thepartial regression coefficients are tested to select explanatoryvariables at a predetermined significance level among the group ofsecond explanatory variables, and the selected explanatory variables areestablished as usable explanatory variables.

The R, G, B signals are then subjected to a matrix of partial regressioncoefficients relative to the usable explanatory variables for conversioninto the colorimetric signals (X, Y, Z) 18.

Since the above process of converting color signals use matrices whichhave a small amount of data, the storage capacity for storing thematrices may be relatively small. Because the group of secondexplanatory variables converted by the principal component analysis isused, the accuracy of color conversion is much better than theconventional matrix process. If the weighting of desired colorscorresponding to certain scene types such as the color of human skin,the color of sky (blue), and the color of vegetables green), among thecolorimetric values of the group of criterion variables is increased,then those desired colors can be converted with greater accuracy. Thedesired colors may be a most frequent color or an average color that isdetermined by statistically processing R, G, B signals of one framesupplied from the digital camera, so that the colors of that frame canbe converted with highest accuracy. If a Macbeth color checker havingcolor patches which represent 24 colors substantially uniformly coveringa color space is used as the color chart, then a conversion matrix forconverting the colors of R, G, B signals accurately into colorimetricvalues can easily be generated.

Because the embodiment employs an improved matrix process, colors can beconverted with high accuracy using a relatively small memory capacity.

Desired colors that have been selected can be converted with higheraccuracy.

If necessary, an existing color chart may be used. For example, if aMacbeth color checker (registered trademark) is used as the color chart,then it is possible to generate, with color patches of 24 colors, amatrix capable of converting colors highly accurately with respect to anoverall color space.

According to the embodiment of the present invention, as describedabove, R, G, B signals 12 which are device-dependent image signals of anoriginal scene captured by a digital camera and/or R. G, B signals 14which are device-dependent image signals of a reversal film subject readby a scanner are converted into colorimetric signals (X, Y, Z) 18, 22which are device-independent image signals by the colorimetric converter16. The colorimetric signals (X, Y, Z) 18, 22 are forwardly convertedinto colorimetric END signals 52 by the colorimetric END forward/inverseconversion matrix 50.

In the forward conversion process, chromaticity coordinates Rxyz, Gxyz,Bxyz of three reference color stimuli R, G, B on a chromaticity diagramare established in a range 73 (see FIG. 4) which contains a colorreproduction range 71.

In order to effect automatic setup calculations in a short period oftime, the rough data generator 54 generate only decimated signals fromthe colorimetric END signals 52.

The automatic setup unit 58 effects an existing automatic setup processon the colorimetric END signals 52 for which gray conditions have beenprescribed. For example, the automatic setup unit 58 generates ahistogram with respect to the colorimetric END signals 52 and thereaftergenerates a cumulative histogram. Certain values of the histogram, e.g.,a value of 0.1% is set to highlight point density (highlight setuppoint) D1 and a value of 98% is set to a shadow point density (shadowsetup point) D2.

The highlight setup point D1 and the shadow setup point D2 areassociated respectively with colorimetric END values corresponding tohighlight halftone dot % and shadow halftone dot %, e.g., DH of 0.1 andDS of 2.0 by a gradation conversion curve 74. Based on the gradationconversion curve 74, the colorimetric END signals 52 are converted intocolorimetric END signals 66 by the END/END converter 64.

Based on the gradation conversion curve 74 which has been established bythe automatic setup process, the gradation-converted colorimetric ENDsignals 66 are inversely converted into the colorimetric signals 18 bythe colorimetric END forward/inverse conversion matrix 50.

As a result, the automatic setup process based on existing densitysignals of the scanner can be effected on the R, G, B signals 12 fromthe digital camera. The highlight point density (highlight setup point)D1 and the shadow point density (shadow setup point) D2 may be set toarbitrary desired values, or stated otherwise, may be determinedmanually, so that a conventional setup process in terms of densities canbe carried out.

Since colorimetric signals are converted into colorimetric END signals,it is possible to effect the setup process thereon in terms ofdensities.

Because when colorimetric signals are converted into colorimetric ENDsignals, they are converted within the reproduction range of thereversal film, it is possible to employ an existing automatic (ormanual) setup process of a linear scanning reader, e.g., a scanner, forimage signals from the reversal subject.

According to the above embodiment, furthermore, for convertingdevice-dependent image signals of an original scene, e.g., R, G, Bsignals 12 captured by a digital camera, into colorimetric signals (X,Y, Z or L*, a*, b*) 18, a color chart having a plurality of colorpatches representing desired typical colors with different chromas,lightnesses, and hues, i.e., a commercially available Macbeth colorchecker, is imaged by the digital camera which is to output thedevice-depending image signals. At least first-order terms of R. G, Bsignals of the imaged color chart are calculated as a group of firstexplanatory variables. The group of first explanatory variables is thensubjected to a principal component analysis for conversion into a groupof second explanatory variables which cross each other. Colorimetricvalues of the color patches of the color chart (color signals in thesecond color space) are used as a group of criterion variables, and thegroup of criterion variables and the group of second explanatoryvariables are subjected to a multiple regression analysis to determine amatrix of partial regression coefficients. Finally, the partialregression coefficients are tested to select explanatory variables at apredetermined significance level among the group of second explanatoryvariables, and the selected explanatory variables are established asusable explanatory variables.

The R, G, B signals are then subjected to a matrix of partial regressioncoefficients relative to the usable explanatory variables for conversioninto the colorimetric signals (X, Y, Z) 18.

Since the above process of converting the R, G, B signals 12 asdevice-dependent image signals into the colorimetric signals 18 asdevice-independent image signals use matrices which have a small amountof data, the storage capacity for storing the matrices may be relativelysmall. Because the group of second explanatory variables converted bythe principal component analysis is used, the accuracy of colorconversion is much better than the matrix process of the colorconversion technique as disclosed in Japanese laid-open patentpublication No. 2-29177 (According to this color conversion technique,R, G, B signals which are not subjected to a principal componentanalysis and their squared terms are used for color conversion with amatrix produced by a multiple regression analysis. Because explanatoryvariables are highly correlated to each other, a multicollineationphenomenon which is an unstable model may be caused statistically, andas a result a low accuracy of color conversion with respect to colorsother than those colors which are used to generate the matrix is notcompensated for).

If a Macbeth color checker having color patches which represent 24colors substantially uniformly covering a color space is used as thecolor chart, then a conversion matrix for converting the colors of R, G,B signals accurately into colorimetric values can easily be generated.

The colorimetric signals 18 of the original scene which have beenconverted by the matrix 20 are converted into c, m, y, k signals whosecolors are colorimetrically save d by the accurate original scenereproduction table 42 which is an association table of c, m, y, ksignals 46 determined with respect to all target values of thecolorimetric signals 18 of the original scene.

Therefore, hues, in particular, of an image on a hard copy that isproduced by the image output unit 35 based on the c, m, y, k signals 46which is produced depending on the R, G, B signals 12, are highlyaccurately representative of the hues of the original scene.

Before the colorimetric signals 18 of the original scene are convertedinto the c, m, y, k signals 46, the colorimetric signals 18 areconverted into colorimetric END signals 52. After the colorimetric ENDsignals 52 are subjected to a setup process and a gradation conversionprocess, they are inversely converted into colorimetric signals 18,which are then converted into c, m, y, k signals 46 whose colors arecolorimetrically saved. Accordingly, it is possible to achieve a desiredimage-dependent finish based on accurately reproduced colors of theoriginal scene.

If necessary, a process of changing the colorimetric signals 18 of theoriginal scene into colorimetric signals 18 under an observing lightsource different from the imaging light source may be added toaccomplish the same color reproduction as under the imaging lightsource.

According to the present invention, therefore, c, m, y, k signals havinghues which are highly similar to those of the original scene can beproduced from the device-dependent image signals that are generated fromthe original scene.

Since the device-dependent image signals are generated from a digitalcamera, for example, it is possible to obtain a hard copy accuratelyrepresentative of the colors of the original scene more easily than theconventional process of generating a hard copy from a reversal film as asubject.

According to the above embodiment, moreover, R, G, B signals 12 of anoriginal scene captured by a digital camera are converted intodevice-independent colorimetric signals 18, which are then convertedinto c, m, y signals 28 which are dye density signals on an exposedreversal film by the dye density converter 30.

The c, m, y signals 28 which are dye density signals on the exposedreversal film have been converted as signals that can be processed inthe same manner as dye density signals (c, m, y signals) 34 on areversal film that has been exposed to signals read by a scanner inputunit. Therefore, the c, m, y signals 28 can easily be converted into c,m, y, k signals 48 which are halftone dot % signals under so-calledcolor-separating default conditions (image processing conditionsincluding a gradation conversion process, a color correction process, asharpness controlling process, an under color removal process, and aK-plate generating process) which are stored as the standard conditionreproduction table 44 in a memory.

Thus, the R, G, B signals 12 outputted from the digital camera caneasily be converted into c, m, y, k signals 48 which are halftone dot %signals.

According to the present invention, device-dependent image signals of anoriginal scene are converted into colorimetric signals, and thecolorimetric signals are then converted into dye density signals on areversal subject.

Consequently, device-dependent image signals of an original scene caneasily be handled by a color-separating system of a scanner (understandard color-separating conditions).

More specifically, inasmuch as signals supplied to a signal processingsystem of a scanner are dye density signals on a reversal subject, evenif the device-dependent image signals are generated by a digital cameraor a scanner, it is possible to output a desired image from the suppliedimage signals, directly using so-called color-separating defaultconditions (image processing conditions including a gradation conversionprocess, a color correction process, an under color removal process, anda K-plate generating process) which are stored as standard conditions ina particular scanner that has been standardized empirically.

Since the color-separating default conditions can be used, theadditional advantage of shortening a period of time required until theimage is outputted is also provided.

According to the above embodiment, furthermore, R, G, B signals 12 of anoriginal image captured by a digital camera are converted intocolorimetric signals (X, Y, Z) 18 by the matrix 20 of the colorimetricconverter 16, and the colorimetric signals (X, Y, Z) 18 are subjected,if necessary, to a setup process by the colorimetric setup unit 26 andconverted into dye density signals (c, m, y) 28 on a reversal subject bythe dye density converter 30.

R, G, B signals 14 obtained from a reversal subject by the scanner inputunit are converted into colorimetric signals (X, Y, Z) 22 by the table24 of the colorimetric converter 16, and the colorimetric signals (X, Y,Z) 22 are subjected, if necessary, to a setup process by thecolorimetric setup unit 26 and converted into dye density signals (c, m,y) 34 by the dye density converter 36.

The dye density signals (c, m, y) 34 supplied through the dye densityconverter 36 to one port of the switch 45 of the cmyk converter 32 aredye density signals on a reversal subject, and the dye density signals(c, m, y) 28 supplied through the dye density converter 30 to the otherport of the switch 45 are also dye density signals on a reversalsubject.

The standard condition reproduction table 44 stores standardcolor-separating default conditions (image processing conditionsincluding a gradation conversion process, a color correction process, anunder color removal process, and a K-plate generating process) forcolor-separating the dye density signals on the reversal subject. Thedye density signals 28, 34 on the reversal subject are color-separatedand converted into c, m, y, k signals which are halftone dot % signalsby the color-separating default conditions.

Therefore, the switch 45 can select one of its input ports depending onwhether image signals are supplied from a digital camera (R, G, Bsignals 12) or a scanner input unit (R, G, B signals 14), and outputsignals from the switch 45 can easily be converted into c, m, y, ksignals which are halftone dot % signals by the common standardcolor-separating default conditions.

According to the present invention, R, G, B signals outputted from adigital camera are converted into colorimetric signals, and then thecolorimetric signals are converted into dye density signals.Furthermore, R, G, B signals outputted from an image reader areconverted into colorimetric signals, and then the colorimetric signalsare converted into dye density signals. The dye density signals are thenprocessed under standard image processing conditions including agradation conversion process, a color correction process, an under colorremoval process, and a K-plate generating process, for conversion intoc, m, y, k signals which are halftone dot % signals.

Even if inputted R, G, B signals are supplied from a digital camera oran image reader, a desired image can be outputted on the basis of the R,G, B signals directly using so-called color-separating defaultconditions which are stored as standard conditions in a particular imagereader, e.g., a scanner, that has been standardized empirically.Inasmuch as the R, G, B signals are converted into device-independentcolorimetric signals, it is possible to output a device-independentimage based on the R, G, B signals.

Although a certain preferred embodiment of the present invention hasbeen shown and described in detail, it should be understood that variouschanges and modifications may be made therein without departing from thescope of the appended claims.

What is claimed is:
 1. A method of converting arbitrary color signals ina first color space into color signals in a second color space,comprising: reading a color chart having a plurality of color patchesrepresenting typical colors with different chromas, lightnesses, andhues with a device which is to output color signals in the first colorspace; determining first-order and second order terms of the read colorsignals in the first color space as a group of first explanatoryvariables; subjecting the group of first explanatory variables to aprincipal component analysis for conversion into a group of secondexplanatory variables which cross each other; using colorimetric valuesof the color patches of the color chart as a group of criterionvariables, and subjecting the group of criterion variables and the groupof second explanatory variables to a multiple regression analysis todetermine a matrix of partial regression coefficients; and subjectingarbitrary color signals in the first color space to the matrix ofpartial regression coefficients for conversion into color signals in thesecond color space.
 2. A method according to claim 1, furthercomprising: after the matrix of partial regression coefficients isdetermined, selecting explanatory variables at a predeterminedsignificance level among the group of second explanatory variables, andestablishing selected explanatory variables as usable explanatoryvariables.
 3. A method according to claim 1, wherein when the matrix ofpartial regression coefficients is determined, a selected color of saidcolor chart is weighted to increase the accuracy with which to convertsaid selected color.
 4. A method according to claim 3, wherein saidselected color comprises a default color of gray.
 5. A method accordingto claim 3, wherein said selected color comprises a color determined bystatistically processing color signals in the first color space of anoriginal scene.
 6. A method according to claim 5, wherein the colorsignals in the first color space are statistically processed byextracting a color of skin, a color of blue, or a color of green fromthe first color space of the original scene.
 7. A method according toclaim 5, wherein the color signals in the first color space arestatistically processed by dividing the first color space into regionsaround the colors of the patches of said color chart, and determiningthe frequencies of pixels, present in the divided regions, of the colorsignals in the first color space of the original scene, and wherein saidparticular color is weighted depending on the frequencies.
 8. A methodaccording to claim 1, wherein said first color space comprises an RGBcolor space, and said second color space comprises a device-independentcolorimetric color space.
 9. A method according to claim 1, wherein saidfirst color space comprises an RGB color space, and said second colorspace comprises a device-independent colorimetric color space, andwherein the first color space is divided into regions around the colorsof the patches of the color chart, R, G, B signals in the first colorspace of an original scene are converted into colorimetric signals bysaid matrix of partial regression coefficients which are not weighted,the frequencies of pixels, present in the regions, of the colorimetricsignals are determined, and a particular color of the color chart isweighted to increase the accuracy with which to convert said particularcolor.
 10. A method according to claim 1, wherein said color chartcomprises a Macbeth color checker.
 11. A method of processing an imagesignal, comprising: converting device-dependent image signals producedfrom a non-reversal film subject into colorimetric signals of anoriginal; converting said colorimetric signals into R, G, B signals in aRGB color system and thereafter logarithmically converting ratios ofsaid R, G, B signals to a basic stimulus into colorimetricequivalent-neutral-density signals; generating rough data of saidcolorimetric equivalent-neutral-density signals; setting upautomatically a gradation conversion curve based on said rough data;converting gradations of said colorimetric equivalent-neutral-densitysignals based on said gradation conversion curve; inverselylogarithmically converting the colorimetric equivalent-neutral-densitysignals whose gradations have been converted into R, G, B signals; andconverting the R, G, B signals into colorimetric signals; said RGB colorsystem comprising a range including reference color stimuli andcontaining a color reproduction range of a reversal film, or a rangeincluding reference color stimuli and containing a color reproductionrange of said non-reversal film subject.
 12. A method of processing animage signal, comprising: converting device-dependent image signalsproduced from an original scene into a plurality of colorimetric signalsof said original scene; converting said colorimetric signals intocolorimetric equivalent-neutral-density signals; generating rough dataof said colorimetric equivalent-neutral-density signals; setting upautomatically a gradation conversion curve based on said rough data;converting gradations of said colorimetric equivalent-neutral-densitysignals based on said gradation conversion curve; converting thecolorimetric equivalent-neutral-density signals whose gradations havebeen converted into a plurality of colorimetric signals; and convertingsaid converted colorimetric signals into c, m, y, k signals whose colorsare colorimetrically saved.
 13. A method of processing an image signal,comprising: converting device-dependent image signals produced from anoriginal scene into a plurality of colorimetric signals of said originalscene; changing said colorimetric signals into colorimetric signalsunder a light source different from a light source under which saiddevice-dependent image signals have been produced; converting saidcolorimetric signals under the different light source into colorimetricequivalent-neutral-density signals; generating rough data of saidcolorimetric equivalent-neutral-density signals; setting upautomatically a gradation conversion curve based on said rough data;converting gradations of said colorimetric equivalent-neutral-densitysignals based on said gradation conversion curve; converting thecolorimetric equivalent-neutral-density signals whose gradations havebeen converted into a plurality of colorimetric signals; and convertingsaid converted colorimetric signals into c, m, y, k signals whose colorsare colorimetrically saved.
 14. A method according to claim 12, whereinsaid device-dependent image signals comprise R, G, B signals generatedby a digital camera.
 15. A method according to claim 13, wherein saiddevice-dependent image signals comprise R, G, B signals generated by adigital camera.
 16. A method of processing an image signal, comprising:converting device-dependent image signals produced from an originalscene into colorimetric signals of said original scene; converting saidcolorimetric signals into colorimetric equivalent-neutral-densitysignals; generating rough data of said colorimetricequivalent-neutral-density signals; setting up automatically a gradationconversion curve based on said rough data; converting said colorimetricsignals into dye density signals on a reversal subject; processing saiddye density signals under standard image processing conditions includinga gradation conversion process, a color correction process, an undercolor removal process, and a K-plate generating process for conversioninto c, m, y, k signals.
 17. A method of processing an image signal,comprising: converting image signals of a reversal subject read by animage reader into a plurality of colorimetric signals on said reversal;converting said colorimetric signals into colorimetricequivalent-neutral-density signals; generating rough data of saidcolorimetric equivalent-neutral-density signals; setting upautomatically a gradation conversion curve based on said rough data;converting gradations of said colorimetric equivalent neutral densitysignals based on said gradation conversion curve; inverselylogarithmically converting the colorimetric equivalent neutral densitysignals whose gradations have been converted into dye density signals;and processing said dye density signals under standard image processingconditions of said image reader including a gradation conversionprocess, a color correction process, an under color removal process, anda K-plate generating process for conversion into c, m, y, k signals. 18.A method according to claim 16, wherein said step of processing said dyedensity signals under standard image processing conditions comprisesprocessing a regular array of color patches of colorimetric values orsignals convertible into the colorimetric values on the reversal subjectunder said standard image processing conditions to generate a cmykimage, generating a data set representing an association between patchesreproduced on said cmyk image and said colorimetric values or saidsignals convertible into the colorimetric values, generating a standardimage processing condition table from said data set, and converting saiddye density signals into the c, m, y, k signals according to saidstandard image processing condition table and an interpolation process.19. A method according to claim 17, wherein said step of processing saiddye density signals under standard image processing conditions comprisesprocessing a regular array of color patches of colorimetric values orsignals convertible into the colorimetric values on the reversal subjectunder said standard image processing conditions to generate a cmykimage, generating a data set representing an association between patchesreproduced on said cmyk image and said colorimetric values or saidsignals convertible into the colorimetric values, generating a standardimage processing condition table from said data set, and converting saiddye density signals into the c, m, y, k signals according to saidstandard image processing condition table and an interpolation process.20. A method of converting arbitrary color signals in a first colorspace into color signals in a second color space, comprising: reading acolor chart having a plurality of color patches representing typicalcolors with different chromas, lightnesses, and hues with a device whichis to output color signals in the first color space; determining firstorder and second order terms of the read color signals in the firstcolor space as a group of first explanatory variables; subjecting thegroup of first explanatory variables to a principal component analysisfor conversion into a group of second explanatory variables, whereineach of said second explanatory variables are not correlated to eachother to avoid a multicollineation effect during multiple regressionanalysis; using colorimetric values of the color patches of the colorchart as a group of criterion variables, and subjecting the group ofcriterion variables and the group of second explanatory variables to amultiple regression analysis to determine a matrix of partial regressioncoefficients; and subjecting arbitrary color signals in the first colorspace to the matrix of partial regression coefficients for conversioninto color signals in the second color space.
 21. A method according toclaim 20, wherein when the matrix of partial regression coefficients isdetermined, a selected color of said color chart is weighted to increasethe accuracy with which to convert the selected color.
 22. A methodaccording to claim 21, wherein said selected color comprises a colordetermined by statistically processing color signals in the first colorspace of an original scene.
 23. A method according to claim 22, whereinthe color signals in the first color space are statistically processedby dividing the first color space into regions around the colors of thepatches of said color chart, and determining the frequencies of pixels,present in the divided regions, of the color signals in the first colorspace of the original scene, and wherein said particular color isweighted depending on the frequencies.